Adaptive digital filters are known for use in various signal processing applications including speech and radar processing, adaptive beamforming, echo cancellation and equalization. These filters estimate the optimal filter coefficients from observed data, i.e. data representing the received signal to be processed.
Reduced rank filters estimate the filter coefficients with a relatively small amount of observed data. Various reduced rank techniques are known including Principal Components, Cross-Spectral and Partial Despreading methods. The former two methods require an explicit estimate of the signal subspace via an eigen-decomposition of the input covariance matrix which is extremely complex. Although the latter technique is much simpler, it does not achieve near full rank performance when the filter rank D is significantly less than the full rank N.
Other known reduced rank techniques include a multi-stage Wiener filter as proposed by Goldstein et al. in “A Multistage Representation Of The Wiener Filter Based On Orthogonal Projections” IEEE Trans. Inform. Theory, 44 (7), November, 1998 and an adaptive interference suppression algorithm as proposed by Honig et al. in “Adaptive Reduced-Rank Residual Correlation Algorithms For DS-CDMA Interference Suppression” In Proc. 32 Asilomar Conf. Signals, Syst. Comput., November, 1998. These methods perform well and do not require an eigen-decomposition. The present invention is an improvement of these latter two methods.